Data from Tumor Evolution Inferred by Patterns of microRNA Expression through the Course of Disease, Therapy, and Recurrence in Breast Cancer

Abstract

<div>Abstract<p><b>Purpose:</b> Molecular evolution of tumors during progression, therapy, and metastasis is a major clinical challenge and the main reason for resistance to therapy. We hypothesized that microRNAs (miRNAs) that exhibit similar variation of expression through the course of disease in several patients have a significant function in the tumorigenic process.</p><p><b>Experimental design:</b> Exploration of evolving disease by profiling 800 miRNA expression from serial samples of individual breast cancer patients at several time points: pretreatment, posttreatment, lymph nodes, and recurrence sites when available (58 unique samples from 19 patients). Using a dynamic approach for analysis, we identified expression modulation patterns and classified varying miRNAs into one of the eight possible temporal expression patterns.</p><p><b>Results:</b> The various patterns were found to be associated with different tumorigenic pathways. The dominant pattern identified an miRNA set that significantly differentiated between disease stages, and its pattern in each patient was also associated with response to therapy. These miRNAs were related to tumor proliferation and to the cell-cycle pathway, and their mRNA targets showed anticorrelated expression. Interestingly, the level of these miRNAs was lowest in matched recurrent samples from distant metastasis, indicating a gradual increase in proliferative potential through the course of disease. Finally, the average expression level of these miRNAs in the pretreatment biopsy was significantly different comparing patients experiencing recurrence to recurrence-free patients.</p><p><b>Conclusions:</b> Serial tumor sampling combined with analysis of temporal expression patterns enabled to pinpoint significant signatures characterizing breast cancer progression, associated with response to therapy and with risk of recurrence. <i>Clin Cancer Res; 22(14); 3651–62. ©2016 AACR</i>.</p></div>

Chemoresistome Mapping in Individual Breast Cancer Patients Unravels Diversity in Dynamic Transcriptional Adaptation

“…This divergence in dynamics led us to hypothesize that classifying genes by their expression pattern can identify resistance-related genes, as depicted in a scheme in Figure 2B. We thus performed dynamic pattern analysis for the differentially expressed genes, similar to our previous studies [29,30], exploiting the strength of the longitudinal dataset. We defined eight possible theoretical patterns (P1-P8) of gene expression modulations through the matched triplet of normal-tumor-treatment stages (Figure 2C).…”

“…To analyze the modification of genes across the three time points (normal, pre-treatment and posttreatment) we defined all possible eight theoretical patterns (Figure 2C) as was previously published [29].…”

“…To understand the personal route to resistance we applied a patient-oriented pattern analysis algorithm for dealing with longitudinal datasets. We previously developed this pattern analysis approach to investigate miRNA expression modulations following recurrence [29], and further applied it to a longitudinal proteomic NAT dataset of breast cancer patients, revealing the involvement of proline biosynthesis in resistance [30].…”